The statements in this section may serve as a background to help understand the invention and its application and uses, but may not constitute prior art.
Conversational or natural language (NL) user interfaces (UIs) can include systems and methods that can rely upon textual, audio, and/or video inputs to allow end users to interact with computing systems using natural language. Performing natural language processing using a computing device can include interpreting human text or speech (e.g., a user's input received via text, speech, or video) utilizing one or more algorithms. In some examples, natural language user interfaces can serve to allow for richer and more fluid interactions between machines and humans than traditional interfaces or existing graphical user interfaces (GUIs), which primarily rely upon keyboard and mouse interactions.
Virtual assistant platforms that utilize conversational or natural language input from end users to automate business tasks have many business applications, including retail sales, customer order management, and many other types of customer service requirements across a range of industries. Conventional virtual assistant platforms implement natural language conversations with end users in one of several ways, either via decision trees or finite state machines, menu-driven approaches, frame-slot approaches, or machine learning on existing conversation datasets.
Firstly, decision trees or finite state machine approaches are highly rigid in their architecture and require extensive developer and subject matter expert development time, require an exponentially exploding tree or finite state machine table, and ultimately lead to a fragile system that results in end user frustration. Secondly, for multi-service conversation systems, traditional menu-driven approaches are similarly highly restrictive in their ability to handle natural conversations, are highly rigid in their conversations with end users, and also result in significant user frustrations. Frame-slot approaches to natural language conversations are also rigid, and similarly have not resulted in flexible conversation systems. Finally, more recent work on machine learning, and specifically deep learning, on existing conversation datasets has shown some promise, but has not led to successful commercial applications because the space of possible conversations corresponding to even a small set of business tasks explodes exponentially.
Therefore, in view of the aforementioned difficulties, there is an unsolved need to provide an enhanced virtual assistant platform and natural language interface for user interactions with computing systems that provides for a flexible and fluid conversation experience. In addition, it would be an advancement in the state of the art of natural language systems to provide systems and methods to enhance the experience of developers of such systems, such that an entire virtual assistant platform can be implemented by subject matter experts without relying on extensive programming or expensive software development efforts, in a matter of hours instead of months or years as compared to conventional approaches.
It is against this background that various embodiments of the present invention were developed.